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- W2328546145 abstract "A great number of functional evaluations may be required until convergence in the process of optimization, especially for the multidisciplinary optimization. Although approximation models constructed by the response surface methodology, which may greatly save the function evaluation, is usually used to conduct a trade off model for optimal designs, it is thought that the design accuracy may be dependent on the type of activation functions and the design region of interest. In this paper, techniques to search the region of interest containing the global optimal design by random seeds, and techniques for finding more accurate approximation using Holographic Neural Network (HNN) is investigated. Furthermore, the mapping method of extrapolation is proposed to make the technique available to general application in structural optimization and the formula to estimate the necessary function evaluations for functions under certain condition is presented. Application examples show that HNN may be expected an potential activate and feasible surface functions in response surface methodology than the polynomials in function approximations. Finally, the real design example of a vehicle component crashworthiness is used to show the effectiveness of the proposed method." @default.
- W2328546145 created "2016-06-24" @default.
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- W2328546145 date "1998-08-22" @default.
- W2328546145 modified "2023-09-23" @default.
- W2328546145 title "Structural optimization based on holographic neural network and its extrapolation" @default.
- W2328546145 cites W2000836282 @default.
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- W2328546145 doi "https://doi.org/10.2514/6.1998-4975" @default.
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